ArticleEntropy (Basel, Switzerland)2023
Forward-Backward Sweep Method for the System of HJB-FP Equations in Memory-Limited Partially Observable Stochastic Control.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Understanding Nash epidemics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Rational social distancing policy during epidemics with limited healthcare capacity.PLoS computational biology · 2023Article
- Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach.Entropy (Basel, Switzerland) · 2023Article
- Article
- Memory-Limited Partially Observable Stochastic Control and Its Mean-Field Control Approach.Entropy (Basel, Switzerland) · 2022Article
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2 authors.
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Abstract
Memory-limited partially observable stochastic control (ML-POSC) is the stochastic optimal control problem under incomplete information and memory limitation. To obtain the optimal control function of ML-POSC, a system of the forward Fokker-Planck (FP) equation and the backward Hamilton-Jacobi-Bellman (HJB) equation needs to be solved. In this work, we first show that the system of HJB-FP equations can be interpreted via Pontryagin's minimum principle on the probability density function space. Based on this interpretation, we then propose the forward-backward sweep method (FBSM) for ML-POSC. FBSM is one of the most basic algorithms for Pontryagin's minimum principle, which alternately computes the forward FP equation and the backward HJB equation in ML-POSC. Although the convergence of FBSM is generally not guaranteed in deterministic control and mean-field stochastic control, it is guaranteed in ML-POSC because the coupling of the HJB-FP equations is limited to the optimal control function in ML-POSC.
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